This article uses data from the 1986 Canadian Family Expenditures Survey to estimate a life-cycle-consistent model of household labor supply and commodity demand that incorporates a mortgage qualification constraint based on earnings. Both the parametric and nonparametric implications of the model suggest that the labor supply of a nontrivial percentage of married women is constrained by mortgage commitments. The results of generalized selectivity models of female labor force participation and labor supply show that the positive effect of a high debt service ratio exceeds the negative effect of young children.

Exploiting differences across U.S. states, this paper demonstrates that there is a tight link between higher education policies, past enrollment rates, and recent changes in the college wage premium among labor market entrants. The analysis reveals, however, that this relationship is much weaker in states with high private enrollment rates, high levels of interstate mobility, or interstate trade. The within-state estimates of the own-cohort relative supply effect shed some light on the extent to which the U.S. labor market can be characterized as a single national market or a collection of state-specific labor markets.

This paper examines the role of spillover effects of minimum wages and threat effects of unionization in changes in wage inequality in the United States between 1979 and 2017. A distribution regression framework is introduced to estimate both types of spillover effects. Threat effects double the contribution of deunionization to the increase in male wage inequality. Spillover effects magnify the explanatory power of declining minimum wages to two-thirds of the increase in inequality at the bottom end of the female wage distribution.

* Department of Economics, University of British Columbia, Vancouver BC, V6T 1Z1, and Department of Economics, CRDE, CIRANO, Universite de Montreal, Montreal, PQ H3C 3J7; Lemieux is also affiliated with the NBER. We thank Paul Beaudry, Richard Blundell, Richard Freeman, and Robert Haveman for helpful comments and discussions. We gratefully acknowledge financial support from the Social Sciences and Humanities Council of Canada and from FCAR, Quebec. 1 The gender wage ratio and the 90/50 wage ratio for men are calculated from the Current Population Survey (CPS) data provided by EPI ^http://www.epinet.org/&. The 1980’s trends in the gender gap emerged from average real increases of 0.5 percent per year in women’s median wages and average real declines of 1 percent per year in men’s median wages.

This paper presents a semiparametric procedure to analyze the effects of institutional and labor market factors on recent changes in the U.S. distribution of wages. The effects of these factors are estimated by applying kernel density methods to appropriately weighted samples. The procedure provides a visually clear representation of where in the density of wages these various factors exert the greatest impact. Using data from the Current Population Survey, we find, as in previous research, that de-unionization and supply and demand shocks were important factors in explaining the rise in wage inequality from 1979 to 1988. We find also compelling visual and quantitative evidence that the decline in the real value of the minimum wage explains a substantial proportion of this increase in wage inequality, particularly for women. We conclude that labor market institutions are as important as supply and demand considerations in explaining changes in the U.S. distribution of wages from 1979 to 1988.

Women's Wages in Women's Work: A U.S./Canada Comparison of the Roles of Unions and "Public Goods" Sector Jobs by Michael Baker and Nicole M. Fortin. Published in volume 89, issue 2, pages 198-203 of American Economic Review, May 1999

We propose a new regression method to evaluate the impact of changes in the distribution of the explanatory variables on quantiles of the unconditional (marginal) distribution of an outcome variable. The proposed method consists of running a regression of the (recentered) influence function (RIF) of the unconditional quantile on the explanatory variables. The influence function, a widely used tool in robust estimation, is easily computed for quantiles, as well as for other distributional statistics. Our approach, thus, can be readily generalized to other distributional statistics.